{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "33016161",
   "metadata": {},
   "source": [
    "# 任务二"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206ad385",
   "metadata": {},
   "source": [
    "统计**不同时段的速度**。数据源--清洗后的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "15d02e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def differentSpeed():\n",
    "\n",
    "    df = pd.read_csv(r'./data/Taxi_sz_data.csv')\n",
    "#     df = df[-(df['Speed'] == 0)]\n",
    "    #删除Speed列含有0的数据行\n",
    "    df['Speed'] = df['Speed'].replace(0,np.nan)\n",
    "    df.dropna()\n",
    "\n",
    "    # 假设 df 包含以时间格式表示的 'Stime' 列\n",
    "    df['Stime'] = pd.to_datetime(df['Stime'], format='%H:%M:%S')\n",
    "\n",
    "    # 为不同时间段创建区间\n",
    "    bins = pd.cut(df['Stime'].dt.hour, bins=[8, 10, 12, 14, 16, 18, 20, 22, 24], labels=[\n",
    "                  '8-10', '10-12', '12-14', '14-16', '16-18', '18-20', '20-22', '22-24'])\n",
    "\n",
    "    # 计算每个时间段的最大、平均和最小速度\n",
    "    grouped_df = df.groupby(bins)['Speed'].agg(\n",
    "        ['max', 'mean', 'min']).reset_index()\n",
    "    # 创建新的 DataFrame 以适应 plot 函数\n",
    "    data = pd.DataFrame({\n",
    "        '时间段': ['8-10', '10-12', '12-14', '14-16', '16-18', '18-20', '20-22', '22-24'],\n",
    "        '最快速度': grouped_df['max'].tolist(),\n",
    "        '平均速度': grouped_df['mean'].tolist(),\n",
    "        '最小速度': grouped_df['min'].tolist()\n",
    "    })\n",
    "    #设置中文显示\n",
    "    plt.rcParams['font.sans-serif']=['simsun']\n",
    "    # 绘图\n",
    "    data.set_index('时间段').plot(kind='bar', rot=0)\n",
    "    plt.xlabel('时间段', fontproperties='simsun')\n",
    "    plt.ylabel('速度', fontproperties='simsun')\n",
    "    #设置y轴刻度\n",
    "    plt.ylim([0,150])\n",
    "    plt.title('不同时间段的速度统计', fontproperties='simsun')\n",
    "#     plt.rcParams['font.family'] = 'simsun'\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c45cfa34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "differentSpeed()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55a6b31",
   "metadata": {},
   "source": [
    "# 任务三"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0da65b0",
   "metadata": {},
   "source": [
    "不同时间段载有乘客的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b59c2fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据源---Taxi_sz.csv\n",
    "import pandas as pd\n",
    "from pylab import mpl\n",
    "data_se = pd.read_csv('./data/Taxi_sz.csv')\n",
    "# 提取小时值\n",
    "data_se['Hour'] = data_se['Stime'].str.slice(0, 2)\n",
    "# 转换数据结构\n",
    "hourCount = data_se.groupby(\n",
    "    'Hour')['VehicleNum'].count().rename('count').reset_index()\n",
    "# 分析绘图\n",
    "mpl.rcParams[\"font.sans-serif\"] = ['SimSun']\n",
    "plt.xlabel('时间/h')\n",
    "plt.ylabel('车数/辆')\n",
    "plt.title('各个时间段行驶的有乘客的车辆数目')\n",
    "plt.plot(hourCount['Hour'], hourCount['count'])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb87bf19",
   "metadata": {},
   "source": [
    "# 任务四--任务--统计不同车辆的平均行驶速度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "449913a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "#读取数据\n",
    "data_clean = pd.read_csv(r'./data/TaxiData-Clean.csv')\n",
    "#自定义函数判断车牌号是否正确\n",
    "def isVehicle(VehicleNum):\n",
    "    taxiDataVehicleNum = data_clean['VehicleNum'].unique()\n",
    "    return VehicleNum in taxiDataVehicleNum\n",
    "\n",
    "#自定义函数求平均速度\n",
    "def meanSpeed(VehicleNum):\n",
    "    i=VehicleNum\n",
    "    num=1\n",
    "    while (isVehicle(i)==False & num<4):\n",
    "        print('车辆牌号不存在，请重新输入（共有 3 次机会）！')\n",
    "        i=eval(input())\n",
    "        num+=1\n",
    "        if num==3:\n",
    "            print(\"机会已用完\")\n",
    "        else:\n",
    "            continue\n",
    "    #提取小时信息\n",
    "    data_clean['Hour'] = data_clean['Stime'].str.slice(0, 2)\n",
    "    #筛选车辆信息\n",
    "    taxiDataByVehicleNum = data_clean[(data_clean['VehicleNum'] == i)]\n",
    "    #计算平均速度\n",
    "    meanSpeed = taxiDataByVehicleNum.groupby(\n",
    "        'Hour')['Speed'].mean().rename('vehicleSpeedMean').reset_index()\n",
    "    # 开始画图\n",
    "    #设置中文显示\n",
    "    mpl.rcParams[\"font.sans-serif\"] = ['SimHei']\n",
    "    mpl.rcParams[\"axes.unicode_minus\"] = False\n",
    "    plt.xlabel(\"时间/小时\")\n",
    "    plt.ylabel(\"平均速度\")\n",
    "    plt.title(\"车牌号为:\"+VehicleNum+\"出租车的平均速度\")\n",
    "    plt.plot(meanSpeed['Hour'], meanSpeed['vehicleSpeedMean'])\n",
    "    plt.show()\n",
    "\n",
    "#实现函数\n",
    "vehicleNum = input(\"请输入车牌号：\")\n",
    "meanSpeed(vehicleNum) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1d77bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>Stime</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Lat</th>\n",
       "      <th>OpenStatus</th>\n",
       "      <th>Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22271</td>\n",
       "      <td>00:00:49</td>\n",
       "      <td>114.266502</td>\n",
       "      <td>22.728201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22271</td>\n",
       "      <td>00:01:48</td>\n",
       "      <td>114.266502</td>\n",
       "      <td>22.728201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22271</td>\n",
       "      <td>00:02:47</td>\n",
       "      <td>114.266502</td>\n",
       "      <td>22.728201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22271</td>\n",
       "      <td>00:03:46</td>\n",
       "      <td>114.266502</td>\n",
       "      <td>22.728201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22271</td>\n",
       "      <td>00:04:45</td>\n",
       "      <td>114.268898</td>\n",
       "      <td>22.729500</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598861</th>\n",
       "      <td>36934</td>\n",
       "      <td>23:49:54</td>\n",
       "      <td>114.056702</td>\n",
       "      <td>22.595800</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598862</th>\n",
       "      <td>36934</td>\n",
       "      <td>23:50:42</td>\n",
       "      <td>114.060303</td>\n",
       "      <td>22.603701</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598863</th>\n",
       "      <td>36934</td>\n",
       "      <td>23:52:32</td>\n",
       "      <td>114.069199</td>\n",
       "      <td>22.614500</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598864</th>\n",
       "      <td>36934</td>\n",
       "      <td>23:53:02</td>\n",
       "      <td>114.067398</td>\n",
       "      <td>22.618401</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598865</th>\n",
       "      <td>36934</td>\n",
       "      <td>23:53:32</td>\n",
       "      <td>114.067299</td>\n",
       "      <td>22.621599</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1598866 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         VehicleNum     Stime         Lng        Lat  OpenStatus  Speed\n",
       "0             22271  00:00:49  114.266502  22.728201           0      0\n",
       "1             22271  00:01:48  114.266502  22.728201           0      0\n",
       "2             22271  00:02:47  114.266502  22.728201           0      0\n",
       "3             22271  00:03:46  114.266502  22.728201           0      0\n",
       "4             22271  00:04:45  114.268898  22.729500           0     11\n",
       "...             ...       ...         ...        ...         ...    ...\n",
       "1598861       36934  23:49:54  114.056702  22.595800           0     36\n",
       "1598862       36934  23:50:42  114.060303  22.603701           0     40\n",
       "1598863       36934  23:52:32  114.069199  22.614500           0     31\n",
       "1598864       36934  23:53:02  114.067398  22.618401           0     32\n",
       "1598865       36934  23:53:32  114.067299  22.621599           0      7\n",
       "\n",
       "[1598866 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.read_csv(r'./data/TaxiData-Clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "face060c",
   "metadata": {},
   "source": [
    "# 作业"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d702fc31",
   "metadata": {},
   "source": [
    "汽车贷款违约的数据分析。 数据源--data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "44cb31c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_id</th>\n",
       "      <th>account_number</th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>vehicle_year</th>\n",
       "      <th>vehicle_make</th>\n",
       "      <th>bankruptcy_ind</th>\n",
       "      <th>tot_derog</th>\n",
       "      <th>tot_tr</th>\n",
       "      <th>age_oldest_tr</th>\n",
       "      <th>tot_open_tr</th>\n",
       "      <th>...</th>\n",
       "      <th>purch_price</th>\n",
       "      <th>msrp</th>\n",
       "      <th>down_pyt</th>\n",
       "      <th>loan_term</th>\n",
       "      <th>loan_amt</th>\n",
       "      <th>ltv</th>\n",
       "      <th>tot_income</th>\n",
       "      <th>veh_mileage</th>\n",
       "      <th>used_ind</th>\n",
       "      <th>weight</th>\n",
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       "      <td>1998.0</td>\n",
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       "      <td>2000.0</td>\n",
       "      <td>DAEWOO</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>10500.00</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>19600.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8725187</td>\n",
       "      <td>15359</td>\n",
       "      <td>1</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>N</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>12100.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "      <td>60</td>\n",
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       "      <th>5840</th>\n",
       "      <td>2291068</td>\n",
       "      <td>10005156</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>PORSCHE</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>417.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>31000.00</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>45000.0</td>\n",
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       "      <td>7647192</td>\n",
       "      <td>10005616</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24970.00</td>\n",
       "      <td>22024.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60</td>\n",
       "      <td>24970.00</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
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       "      <td>5993246</td>\n",
       "      <td>10006591</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>N</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>20949.00</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1837.50</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.75</td>\n",
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       "    <tr>\n",
       "      <th>5843</th>\n",
       "      <td>4766566</td>\n",
       "      <td>10010208</td>\n",
       "      <td>0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>MERCURY</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22400.00</td>\n",
       "      <td>28700.0</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>48</td>\n",
       "      <td>17100.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>28000.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5844</th>\n",
       "      <td>1928782</td>\n",
       "      <td>10010219</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>JEEP</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19609.90</td>\n",
       "      <td>20145.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52</td>\n",
       "      <td>19609.90</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5845 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      application_id  account_number  bad_ind  vehicle_year vehicle_make  \\\n",
       "0            2314049           11613        1        1998.0         FORD   \n",
       "1              63539           13449        0        2000.0       DAEWOO   \n",
       "2            7328510           14323        1        1998.0     PLYMOUTH   \n",
       "3            8725187           15359        1        1997.0         FORD   \n",
       "4            4275127           15812        0        2000.0       TOYOTA   \n",
       "...              ...             ...      ...           ...          ...   \n",
       "5840         2291068        10005156        0        1997.0      PORSCHE   \n",
       "5841         7647192        10005616        0        2000.0       TOYOTA   \n",
       "5842         5993246        10006591        0        1997.0    CHEVROLET   \n",
       "5843         4766566        10010208        0        1999.0      MERCURY   \n",
       "5844         1928782        10010219        0        2000.0         JEEP   \n",
       "\n",
       "     bankruptcy_ind  tot_derog  tot_tr  age_oldest_tr  tot_open_tr  ...  \\\n",
       "0                 N        7.0     9.0           64.0          2.0  ...   \n",
       "1                 N        0.0    21.0          240.0         11.0  ...   \n",
       "2                 N        7.0    10.0           60.0          NaN  ...   \n",
       "3                 N        3.0    10.0           35.0          5.0  ...   \n",
       "4                 N        0.0    10.0          104.0          2.0  ...   \n",
       "...             ...        ...     ...            ...          ...  ...   \n",
       "5840              N        0.0    21.0          417.0          4.0  ...   \n",
       "5841              Y        2.0     8.0           62.0          5.0  ...   \n",
       "5842              N        0.0     6.0           30.0          4.0  ...   \n",
       "5843              N        0.0     9.0           67.0          7.0  ...   \n",
       "5844              N        1.0    34.0          130.0          8.0  ...   \n",
       "\n",
       "      purch_price     msrp  down_pyt  loan_term  loan_amt    ltv  tot_income  \\\n",
       "0        17200.00  17350.0      0.00         36  17200.00   99.0     6550.00   \n",
       "1        19588.54  19788.0    683.54         60  19588.54   99.0     4666.67   \n",
       "2        13595.00  11450.0      0.00         60  10500.00   92.0     2000.00   \n",
       "3        12999.00  12100.0   3099.00         60  10800.00  118.0     1500.00   \n",
       "4        26328.04  22024.0      0.00         60  26328.04  122.0     4144.00   \n",
       "...           ...      ...       ...        ...       ...    ...         ...   \n",
       "5840         0.00  31000.0      0.00         36  31000.00  100.0     5000.00   \n",
       "5841     24970.00  22024.0      0.00         60  24970.00  117.0     2400.00   \n",
       "5842     20949.00  18950.0      0.00         36  20949.00  113.0     1837.50   \n",
       "5843     22400.00  28700.0   5300.00         48  17100.00   60.0    28000.00   \n",
       "5844     19609.90  20145.0      0.00         52  19609.90   97.0     2700.00   \n",
       "\n",
       "      veh_mileage  used_ind  weight  \n",
       "0         24000.0         1    1.00  \n",
       "1            22.0         0    4.75  \n",
       "2         19600.0         1    1.00  \n",
       "3         10000.0         1    1.00  \n",
       "4            14.0         0    4.75  \n",
       "...           ...       ...     ...  \n",
       "5840      45000.0         1    4.75  \n",
       "5841         21.0         0    4.75  \n",
       "5842      25000.0         1    4.75  \n",
       "5843          0.0         0    4.75  \n",
       "5844         12.0         0    4.75  \n",
       "\n",
       "[5845 rows x 25 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as  pd \n",
    "data = pd.read_csv('./data/data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "331c5167",
   "metadata": {},
   "source": [
    "'申请者ID','帐户号','是否违约','汽车购买时间','汽车制造商','曾经破产标识','五年内信用不良事件数量','全部帐户数量', '账号存续月份数','开户帐户数量','信用卡数量','信用卡欠款余额','信用卡授信额度','信用卡额度使用比例', 'FICO打分','汽车购买金额','建议售价','分期付款的首次交款','贷款期限','贷款金额','贷款金额/售价','月均收入','行驶里程','是否二手车','样本权重'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f801e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_id</th>\n",
       "      <th>account_number</th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>vehicle_year</th>\n",
       "      <th>vehicle_make</th>\n",
       "      <th>bankruptcy_ind</th>\n",
       "      <th>tot_derog</th>\n",
       "      <th>tot_tr</th>\n",
       "      <th>age_oldest_tr</th>\n",
       "      <th>tot_open_tr</th>\n",
       "      <th>...</th>\n",
       "      <th>purch_price</th>\n",
       "      <th>msrp</th>\n",
       "      <th>down_pyt</th>\n",
       "      <th>loan_term</th>\n",
       "      <th>loan_amt</th>\n",
       "      <th>ltv</th>\n",
       "      <th>tot_income</th>\n",
       "      <th>veh_mileage</th>\n",
       "      <th>used_ind</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2314049</td>\n",
       "      <td>11613</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>N</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>17350.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>99.0</td>\n",
       "      <td>6550.00</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63539</td>\n",
       "      <td>13449</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>DAEWOO</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19588.54</td>\n",
       "      <td>19788.0</td>\n",
       "      <td>683.54</td>\n",
       "      <td>60</td>\n",
       "      <td>19588.54</td>\n",
       "      <td>99.0</td>\n",
       "      <td>4666.67</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8725187</td>\n",
       "      <td>15359</td>\n",
       "      <td>1</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>3099.00</td>\n",
       "      <td>60</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4275127</td>\n",
       "      <td>15812</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26328.04</td>\n",
       "      <td>22024.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60</td>\n",
       "      <td>26328.04</td>\n",
       "      <td>122.0</td>\n",
       "      <td>4144.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8712513</td>\n",
       "      <td>16979</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26272.72</td>\n",
       "      <td>26375.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>26272.72</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5400.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5840</th>\n",
       "      <td>2291068</td>\n",
       "      <td>10005156</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>PORSCHE</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>31000.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5841</th>\n",
       "      <td>7647192</td>\n",
       "      <td>10005616</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24970.00</td>\n",
       "      <td>22024.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60</td>\n",
       "      <td>24970.00</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5842</th>\n",
       "      <td>5993246</td>\n",
       "      <td>10006591</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20949.00</td>\n",
       "      <td>18950.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>20949.00</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1837.50</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5843</th>\n",
       "      <td>4766566</td>\n",
       "      <td>10010208</td>\n",
       "      <td>0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>MERCURY</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22400.00</td>\n",
       "      <td>28700.0</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>48</td>\n",
       "      <td>17100.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>28000.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5844</th>\n",
       "      <td>1928782</td>\n",
       "      <td>10010219</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>JEEP</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19609.90</td>\n",
       "      <td>20145.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52</td>\n",
       "      <td>19609.90</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4105 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      application_id  account_number  bad_ind  vehicle_year vehicle_make  \\\n",
       "0            2314049           11613        1        1998.0         FORD   \n",
       "1              63539           13449        0        2000.0       DAEWOO   \n",
       "3            8725187           15359        1        1997.0         FORD   \n",
       "4            4275127           15812        0        2000.0       TOYOTA   \n",
       "5            8712513           16979        0        2000.0        DODGE   \n",
       "...              ...             ...      ...           ...          ...   \n",
       "5840         2291068        10005156        0        1997.0      PORSCHE   \n",
       "5841         7647192        10005616        0        2000.0       TOYOTA   \n",
       "5842         5993246        10006591        0        1997.0    CHEVROLET   \n",
       "5843         4766566        10010208        0        1999.0      MERCURY   \n",
       "5844         1928782        10010219        0        2000.0         JEEP   \n",
       "\n",
       "     bankruptcy_ind  tot_derog  tot_tr  age_oldest_tr  tot_open_tr  ...  \\\n",
       "0                 N        7.0     9.0           64.0          2.0  ...   \n",
       "1                 N        0.0    21.0          240.0         11.0  ...   \n",
       "3                 N        3.0    10.0           35.0          5.0  ...   \n",
       "4                 N        0.0    10.0          104.0          2.0  ...   \n",
       "5                 Y        2.0    15.0          136.0          4.0  ...   \n",
       "...             ...        ...     ...            ...          ...  ...   \n",
       "5840              N        0.0    21.0          417.0          4.0  ...   \n",
       "5841              Y        2.0     8.0           62.0          5.0  ...   \n",
       "5842              N        0.0     6.0           30.0          4.0  ...   \n",
       "5843              N        0.0     9.0           67.0          7.0  ...   \n",
       "5844              N        1.0    34.0          130.0          8.0  ...   \n",
       "\n",
       "      purch_price     msrp  down_pyt  loan_term  loan_amt    ltv  tot_income  \\\n",
       "0        17200.00  17350.0      0.00         36  17200.00   99.0     6550.00   \n",
       "1        19588.54  19788.0    683.54         60  19588.54   99.0     4666.67   \n",
       "3        12999.00  12100.0   3099.00         60  10800.00  118.0     1500.00   \n",
       "4        26328.04  22024.0      0.00         60  26328.04  122.0     4144.00   \n",
       "5        26272.72  26375.0      0.00         36  26272.72  100.0     5400.00   \n",
       "...           ...      ...       ...        ...       ...    ...         ...   \n",
       "5840         0.00  31000.0      0.00         36  31000.00  100.0     5000.00   \n",
       "5841     24970.00  22024.0      0.00         60  24970.00  117.0     2400.00   \n",
       "5842     20949.00  18950.0      0.00         36  20949.00  113.0     1837.50   \n",
       "5843     22400.00  28700.0   5300.00         48  17100.00   60.0    28000.00   \n",
       "5844     19609.90  20145.0      0.00         52  19609.90   97.0     2700.00   \n",
       "\n",
       "      veh_mileage  used_ind  weight  \n",
       "0         24000.0         1    1.00  \n",
       "1            22.0         0    4.75  \n",
       "3         10000.0         1    1.00  \n",
       "4            14.0         0    4.75  \n",
       "5             1.0         0    4.75  \n",
       "...           ...       ...     ...  \n",
       "5840      45000.0         1    4.75  \n",
       "5841         21.0         0    4.75  \n",
       "5842      25000.0         1    4.75  \n",
       "5843          0.0         0    4.75  \n",
       "5844         12.0         0    4.75  \n",
       "\n",
       "[4105 rows x 25 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#处理数据中的NaN值\n",
    "data.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "efc51577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>bankruptcy_ind</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>4163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>1017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bad_ind bankruptcy_ind  count\n",
       "0        0              N   4163\n",
       "1        1              N   1017\n",
       "2        0              Y    345\n",
       "3        1              Y    103"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bad_bankruptcy = data[['bad_ind','bankruptcy_ind']].value_counts().reset_index()\n",
    "data_bad_bankruptcy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9dabcf9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>bankruptcy_ind</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bad_ind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4163</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1017</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bankruptcy_ind     N    Y\n",
       "bad_ind                  \n",
       "0               4163  345\n",
       "1               1017  103"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_count=data_bad_bankruptcy.pivot(index='bad_ind',columns='bankruptcy_ind',values='count')\n",
    "data_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5358a434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4163, 345, 1017, 103]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计订单违约和未违约,破产和未破产的数量（）\n",
    "data_bad_bankruptcy = data[['bad_ind','bankruptcy_ind']].value_counts().reset_index()\n",
    "data_bad_bankruptcy\n",
    "#违约和未违约的数量列表\n",
    "bad_ind_count = []\n",
    "#破产和未破产的数量列表\n",
    "bankruptcy_count = []\n",
    "#往列表中追加数据\n",
    "bad_ind_count.append(data_count['N'][0]+data_count['Y'][0])\n",
    "bad_ind_count.append(data_count['N'][1]+data_count['Y'][1])\n",
    "bankruptcy_count.append(data_count['N'][0])\n",
    "bankruptcy_count.append(data_count['Y'][0])\n",
    "bankruptcy_count.append(data_count['N'][1])\n",
    "bankruptcy_count.append(data_count['Y'][1])\n",
    "bankruptcy_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fe632ecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制饼图显示违约和未违约的比例\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#绘制4x4的画布\n",
    "size=0.35\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.pie(bad_ind_count,labels=['未违约','违约'],\n",
    "        autopct='%1.2f%%',textprops={'fontsize': 18, 'color': 'r'},\n",
    "        radius=1,wedgeprops=dict(width=size, edgecolor='w'),\n",
    "       )\n",
    "\n",
    "plt.pie(bankruptcy_count,labels=['未违约但破产','未违约未破产','违约破产','违约未破产'],\n",
    "        autopct='%1.2f%%',textprops={'fontsize': 12, 'color': 'black'},\n",
    "        radius=1-size,wedgeprops=dict(width=size, edgecolor='w'))\n",
    "plt.title('用户违约和未违约的比例分布图')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8438cf71",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1474598263.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[1], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    jupyter notebook --version\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "jupyter notebook --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b5b0adf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
